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										01-Java基础
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										04. 反射和动态代理
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										01. 多线程基础
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										02. CAS
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										03. synchronized
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										04. AQS和ReentrantLock
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										05. 线程池
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										06. ThreadLocal
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										07. CountDownLatch和CyclicBarrier
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										08. 为什么需要Java内存模型
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										09. 深入浅出Java内存模型
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										03-Java容器
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										01. List集合
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										02. Map集合
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										04-Java虚拟机
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										01. Java编译到执行的过程
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										02. 双亲委派机制
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										03. JVM内存结构
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										04. 垃圾回收机制
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										05. CMS垃圾收集器
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										06. G1垃圾收集器
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										07. JVM调优
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										05-Spring
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										01. SpringMVC
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										02. Spring基础
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										03. SpringBean生命周期
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										06-Redis
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										01. Redis基础
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										02. Redis持久化
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										03. Redis主从架构
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										04. Redis分片集群
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										07-消息队列
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										01. Kafka基础
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										02. 使用Kafka会考虑什么问题
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										08-MySQL
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										01. MySQL索引
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										02. MySQL事务和锁机制和MVCC
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										03. MySQL调优
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										09-项目场景类
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										01. 如何实现去重和幂等
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										02. 系统需求多变如何设计
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										03. 设计模式
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										10-计算机网络
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										01. HTTP
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										11-算法
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										01. 排序算法
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	01. Redis基础
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	<span>3y</span>
	<span>2021-08-19 15:48:47</span>
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	<p><span style="color:#ab4642"><strong>面试官</strong></span>：<strong>今天要不来聊聊Redis吧？</strong></p>
<p><span style="color:#000080"><strong>候选者</strong></span>：好</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：我个人是这样理解的：无论Redis也好、MySQL也好、HDFS也好、HBase也好，他们都是存储数据的地方</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：因为它们的设计理念的不同，我们会根据不同的应用场景使用不同的存储</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：像Redis一般我们会把它用作于缓存</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：当然啦，日常有的应用场景比较简单，用个HashMap也能解决很多的问题了，没必要上Redis</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：这就好比，有的单机限流可能应对某些场景就够用了，也没必要说一定要上分布式限流把系统搞得复杂</p>
<p><img src="https://tva1.sinaimg.cn/large/008i3skNgy1gtyvd0afpcj60ty086dg202.jpg"></p>
<p><span style="color:#ab4642"><strong>面试官</strong></span>：<strong>你在项目里有用到Redis吗？怎么用的？</strong></p>
<p><span style="color:#000080"><strong>候选者</strong></span>：Redis肯定是用到的，我负责的项目几乎都会有Redis的踪影</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：我举几个我这边项目用的案例呗？</p>
<p><span style="color:#ab4642"><strong>面试官</strong></span>：嗯</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：我这边负责消息管理平台，简单来说就是发消息的</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：那发完消息肯定我们是得知道消息有没有下发成功的，是吧？</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：于是我们系统有一套完整的链路追踪体系</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：其中实时的数据我们就用Redis来进行存储，有实时肯定就会有离线的嘛（离线的数据我们是存储到Hive的）</p>
<p><img src="https://tva1.sinaimg.cn/large/008i3skNgy1gtyvevn728j60ko0fygm202.jpg"></p>
<p><span style="color:#000080"><strong>候选者</strong></span>：对消息进行实时链路追踪，我这边就用了Redis好几种的数据结构</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：分别有Set、List和Hash</p>
<p><span style="color:#ab4642"><strong>面试官</strong></span>：嗯….</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：我再稍微铺垫下链路追踪的背景吧~</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：要在消息管理平台发消息，首先得在后台新建一个「模板」，有模板自然会有一个模板ID</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：对模板ID进行扩展，比如说加上日期和固定的业务参数，形成的ID可以唯一标识某个模板的下发链路</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：在系统上，我这边叫它为UMPID</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：在发送入口处会对所有需要下发的消息打上UMPID，然后在关键链路上打上对应的点位</p>
<p><img src="https://tva1.sinaimg.cn/large/008i3skNgy1gtyvfly3b7j60q205cglr02.jpg"></p>
<p><span style="color:#ab4642"><strong>面试官</strong></span>：嗯，你继续吧</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：接下来的工作就是清洗出统一的模型，然后根据不同维度进行处理啦。比如说：</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：我要看某一天下发的所有模板有哪些，那只要我把清洗出来后数据的，将对应UMPID扔到了Set就好了</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：我要看某一个模板的消息下发的整体链路情况，那我以UMPID为Key，Value是Hash结构，Key是state，Value则是人数</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：这里的state我们在下发的过程中打的关键点位，比如接收到消息打个51，消息被去重了打个61，消息成功下发了打个81…</p>
<p><img src="https://tva1.sinaimg.cn/large/008i3skNgy1gtyvgkxib0j60pc066aac02.jpg"></p>
<p><span style="color:#000080"><strong>候选者</strong></span>：以UMPID为Key，Hash结构的Key（State）进行不断的累加，就可以实现某一个模板的消息下发的整体链路情况</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：我要看某个用户当天下发的消息有哪些，以及这些消息的整体链路是如何。</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：这边我用的是List结构，Key是userId，Value则是UMPID+state(关键点位)+processTime（处理时间)</p>
<p><span style="color:#ab4642"><strong>面试官</strong></span>：嗯….</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：简单来说，就是通过Redis丰富的数据结构来实现对下发消息多个维度的统计</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：不同的应用场景选择不同的数据结构，再等到透出做处理的时候，就变得十分简单了</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：消息下发过程中去重或者一般正常的场景就直接Key-Value就能符合需求了</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：像bitmap、hyperloglogs、sortset、steam等等这些数据结构在我所负责的项目用得是真不多</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：要是我有机会去到贵公司，贵公司有相关的应用场景，我相信我也很快就能掌握</p>
<p><img src="https://tva1.sinaimg.cn/large/008i3skNgy1gtyvh1wbjoj60pk06w74l02.jpg"></p>
<p><span style="color:#000080"><strong>候选者</strong></span>：这些数据结构底层都由对应的object来支撑着，object记录对应的「编码」</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：其实就是会根据key-value存储的数量或者长度来使用选择不同的底层数据结构实现</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：比如说：ziplist压缩列表这个底层数据结构有可能上层的实现是list、hash和sortset</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：Hash结构的底层数据结构可能是hash和ziplist</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：在节省内存和性能的考量之中切换</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：Redis还是有点屌的啊。</p>
<p><img src="https://tva1.sinaimg.cn/large/008i3skNgy1gtyvhui46sj60py04ut9402.jpg"></p>
<p><span style="color:#ab4642"><strong>面试官</strong></span>：<strong>就你上面那个实时链路场景，可以用其他的存储替代吗？</strong></p>
<p><span style="color:#000080"><strong>候选者</strong></span>：嗯，理论上是可以的（或许可以尝试用HBase），但总体来说没这么好吧</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：因为Redis拥有丰富的数据结构，在透出的时候，处理会非常的方便。</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：如果不用Redis的话，还得做很多解析的工作</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：并且，我那场景的并发还是相当大的（就一条消息发送，可能就产生10条记录）</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：监控峰值命令处理数会去到20k+QPS，当然了，这场景我肯定用了Pipeline的（不然处理会慢很多）</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：综合上面并发量和实时性以及数据结构，用Redis是一个比较好的选择</p>
<p><img src="https://tva1.sinaimg.cn/large/008i3skNgy1gtyw0mlggbj60y405emy302.jpg"></p>
<p><span style="color:#ab4642"><strong>面试官</strong></span>:嗯….<strong>你觉得为什么Redis可以这么快？</strong></p>
<p><span style="color:#000080"><strong>候选者</strong></span>：首先，它是纯内存操作，内存本身就很快</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：其次，它是单线程的，Redis服务器核心是基于非阻塞的IO多路复用机制，单线程避免了多线程的频繁上下文切换问题</p>
<p><span style="color:#000080"><strong>候选者</strong></span>：至于这个单线程，其实官网也有过说明（：表示使用Redis往往的瓶颈在于内与和网络，而不在于CPU</p>
<p><span style="color:#ab4642"><strong>面试官</strong></span>：了解。</p>
<p><img src="https://tva1.sinaimg.cn/large/008i3skNgy1gtm5ekni6pj61400mimyx02.jpg"></p>
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